International Journal of Science, Technology & Society
The journal (International Journal of Science, Technology & Society) which is a peer reviewed research journal is planned to publish the debate, commentary, reviews and original research papers of high quality research and reviews primarily on the issues and knowledge regarding interdisciplinary approach in science and technology and inclusive policies by it can help marginalized sections of the society to become an integral part of main stream. The journal covers new knowledge and new insights in all disciplines of science including its interface with one another and with societies. It is unique in the sense, it makes a bridge between the science, technology and society which is an emerging thrust throughout the world to make an equal, peaceful and sustainable world. There is no page charge and no direct or indirect expenditure involved, but for a meager amount charged, only if the authors intend to buy hard copy of reprints of their paper. The authors will be provided a PDF file of their paper and a copy of the issue in which they have contributed. We invite all the colleagues to contribute their work in this journal in benefit of the science and society. Journal is available on new link: https://ijsts.info/index.php/ijsts/issue/archive
Asynchronous Generative AI: Automating Qualitative Data Workflows in Enterprise Systems
Qualitative data collection in enterprise systems often depends on synchronous interviews, manual screening, and time-consuming stakeholder interactions, creating delays in user research, talent acquisition, and operational decision-making. This article examines how asynchronous generative artificial intelligence can automate qualitative data workflows through voice AI agents, customized graphical user interfaces, and secure enterprise data platforms. The study focuses on the use of conversational AI systems that allow users to provide responses at their own convenience while enabling organizations to collect, structure, and analyze qualitative inputs more efficiently. Drawing on enterprise deployment within the electric vehicle sector, particularly the Rivian case and the AI Fest 2026 prototype, the article shows that asynchronous voice AI can reduce manual screening time, improve access management, and transform unstructured audio input into organized qualitative data. However, the article also emphasizes that automation must be supported by strong role-based access control, data integrity measures, and human-in-the-loop safeguards. The findings suggest that asynchronous generative AI can convert qualitative data collection from a slow operational bottleneck into a scalable, secure, and strategic enterprise workflow.
Asynchronous Generative AI, Voice AI Agents, Qualitative Data Workflows, Enterprise Systems, Role-Based Access Control, Human-in-the-Loop, Graphical User Interface,
Copyright (c) 2026 https://creativecommons.org/licenses/by-sa/4.0
Predictive Learning Analytics Using SAP and AI for Early Identification of Academic Disparities
Academic disparities — systematic performance gaps rooted in socioeconomic, demographic, and institutional factors — represent one of the most persistent challenges in modern education. Traditional approaches to identifying at-risk learners rely on lagging indicators such as end-of-term grades or attendance records, by which point meaningful intervention windows have often closed. This paper presents a Predictive Learning Analytics (PLA) framework that integrates SAP Student Lifecycle Management (SAP SLCM), SAP Analytics Cloud (SAC), and advanced Artificial Intelligence models — including Gradient Boosting classifiers, LSTM temporal networks, and Explainable AI techniques — to enable early, accurate, and equitable identification of academic disparity trajectories. Implemented across three higher education institutions in India and the Gulf region involving 28,740 enrolled students over three academic years, the proposed framework achieved a prediction sensitivity of 89.4% for at-risk identification at the eight-week mark of a semester, a false-positive rate of 6.2%, and a 31.7% reduction in end-of-year academic failure rates among cohorts receiving AI-triggered interventions. Demographic fairness analysis confirmed equitable prediction accuracy across gender, socioeconomic, and regional subgroups (disparity index < 0.06). The study establishes a replicable, ethically governed model for AI-augmented educational equity in SAP-integrated institutional environments.
Predictive Learning Analytics, Academic Disparities, SAP SLCM, SAP Analytics Cloud, Artificial Intelligence, Early Warning Systems, Educational Equity, Gradient Boosting, Explainable AI, At-Risk Student Identification
Copyright (c) 2025 https://creativecommons.org/licenses/by-sa/4.0
Towards Autonomous Environmental Management: Artificial Intelligence for Stormwater, Wastewater, and Urban Water Systems
The convergence of artificial intelligence, autonomous systems, multi-agent coordination, and pervasive urban sensing is propelling environmental management toward a new paradigm: autonomous urban water governance in which AI agents sense, decide, act, and learn across the full stormwater–wastewater–potable water continuum with minimal human intervention in routine operations. This article presents a comprehensive, forward-looking review of the trajectory toward autonomous environmental management, examining both the current state of AI deployment across urban water systems and the near-future developments — large-scale multi-agent systems, foundation models for hydrology, autonomous field robotics, and federated learning at continental scale — that will define the next decade of the field. Drawing on 116 peer-reviewed studies, documented operational deployments, and emerging research prototypes published between 2020 and 2025, we evaluate progress across eight autonomy dimensions: perception and situational awareness, predictive intelligence, autonomous decision-making, adaptive control, self-healing infrastructure, collaborative multi-agent coordination, explainable and auditable autonomy, and ethical autonomous governance. Our analysis reveals a consistent pattern: autonomy advances most rapidly in domains where consequences of error are limited and reversible (sensor data interpretation, predictive alerting), and more slowly where consequences are high-stakes and irreversible (structural interventions, potable water quality decisions). We introduce the Autonomy Readiness Index for Water Systems (ARIWS) — a quantitative framework for assessing the readiness of specific water management decisions for increasing levels of AI autonomy — and demonstrate its application across 24 representative decision contexts spanning stormwater, wastewater, and potable water management. The article critically examines the ethical, legal, and governance dimensions of autonomous water management — including algorithmic accountability, the rights of communities to human-mediated decisions affecting their water security, and the conditions under which full AI autonomy is socially legitimate. We conclude that achieving beneficial autonomous environmental management requires not only continued technical advancement but a co-evolution of regulatory frameworks, professional standards, community engagement practices, and international governance architectures that ensure autonomous systems serve the public interest equitably and accountably.
autonomous environmental management; artificial intelligence; multi-agent systems; urban water systems; stormwater; wastewater; foundation models; autonomous robotics; explainable AI; water governance; federated learning
Artificial Intelligence for Sustainable Environmental Management: Integrating Stormwater and Wastewater Systems
Sustainable environmental management demands a holistic, systems-thinking approach that moves beyond the operational siloes separating stormwater and waxstewater governance. Urban water systems — encompassing stormwater conveyance, combined sewer networks, and centralised wastewater treatment — are intrinsically interconnected, yet historically managed by distinct engineering disciplines, institutional actors, and regulatory frameworks. This institutional fragmentation imposes substantial environmental, economic, and social costs: suboptimal infrastructure investment, missed opportunities for resource recovery, elevated receiving water pollution loads, and inequitable distribution of flood risk and service quality. Artificial intelligence (AI) offers the integrative analytical capacity to bridge these siloes, enabling holistic water system optimisation grounded in sustainability principles. This article presents a comprehensive review of 103 peer-reviewed studies published between 2018 and 2025, examining how machine learning, deep learning, reinforcement learning, multi-agent systems, life cycle assessment-informed AI, and circular economy-oriented decision support tools are transforming the integrated management of stormwater and wastewater systems. We evaluate AI applications across six sustainability dimensions: resource recovery maximisation, energy self-sufficiency, ecological quality protection, social equity and environmental justice, circular economy integration, and governance and institutional capacity. Key findings reveal that AI-optimised resource recovery — including nutrient recapture, biogas generation, water reuse, and heat recovery — can convert WWTPs from net energy consumers to net energy producers in 34–58% of case study contexts, while AI-driven green-grey infrastructure integration reduces combined sewer overflow volumes by 38–61% in reviewed deployments.
sustainable environmental management; artificial intelligence; stormwater integration; wastewater treatment; resource recovery; circular economy; SDGs; green infrastructure; environmental justice; life cycle assessment
Artificial Intelligence Applications in Environmental Management: Integrating Stormwater and Wastewater Systems
The accelerating complexity of urban water management demands a paradigm shift from conventional reactive approaches towards proactive, data-driven governance. This article presents a comprehensive review and synthesis of artificial intelligence (AI) applications at the nexus of stormwater and wastewater management, examining how machine learning, deep learning, Internet of Things (IoT)-enabled sensing networks, digital twin frameworks, and AI-driven decision support systems are reshaping environmental management practice. Drawing on 94 peer-reviewed studies published between 2018 and 2025, we document performance benchmarks across four core application domains: flood forecasting and early warning, wastewater treatment process optimisation, combined sewer overflow prediction, and integrated catchment-scale water quality modelling. Our meta-analysis reveals that ensemble deep learning models consistently outperform standalone methods, reducing root mean square error (RMSE) for short-term runoff prediction by 28–47% relative to physics-based benchmarks. Transformer-based architectures demonstrate exceptional capacity for multi-variate, long-horizon forecasting where temporal dependencies span multiple rainfall seasons. We further identify critical barriers to operational deployment, including sensor data heterogeneity, model interpretability deficits, regulatory inertia, and infrastructure funding asymmetries between high- and low-income municipalities. A proposed Integrated AI Environmental Management (IAEM) framework is presented that harmonises real-time sensing, adaptive control, stakeholder engagement, and regulatory compliance pathways. The article concludes with a research agenda prioritising federated learning for privacy-preserving multi-utility data sharing and physics-informed neural networks for improved generalisation under climate-change-altered hydrological regimes.
artificial intelligence; stormwater management; wastewater treatment; machine learning; deep learning; IoT; digital twins; urban hydrology; environmental informatics; sustainable infrastructure
Artificial Intelligence for Climate-Resilient Stormwater and Wastewater Management
Climate change is fundamentally altering the hydrological regimes upon which stormwater and wastewater infrastructure was designed to operate, manifesting as intensified extreme precipitation events, extended drought cycles, sea-level rise-induced groundwater infiltration, and thermally stressed biological treatment processes. Conventional infrastructure design paradigms, premised on stationary climatological assumptions, are rapidly becoming obsolete. This article presents a comprehensive review of how artificial intelligence (AI) — spanning machine learning, deep learning, reinforcement learning, physics-informed neural networks, and multi-agent systems — is being deployed to build climate resilience into stormwater and wastewater management systems across diverse geographies and institutional contexts. Synthesising 112 peer-reviewed studies published between 2019 and 2025, we evaluate AI performance across five climate-critical domains: non-stationary flood frequency analysis, drought-adaptive wastewater reuse optimisation, sea-level rise and groundwater intrusion modelling, heat-stress resilience in biological wastewater treatment, and equity-weighted infrastructure investment prioritisation under climate uncertainty. Our analysis finds that physics-informed hybrid architectures consistently outperform purely data-driven models when extrapolating beyond historical climate envelopes, with Physics-Informed Neural Networks (PINNs) retaining 73–89% of in-distribution performance accuracy under 2°C and 4°C warming scenarios. We introduce the Climate-Adaptive AI Water Resilience (CAAWR) framework, which operationalises a five-stage resilience cycle — sense, anticipate, adapt, recover, and learn — using AI as the connective tissue between real-time sensing and long-term planning. Critical findings include the persistent underrepresentation of Global South case studies (23% of reviewed literature), the urgent need for climate-downscaled training datasets at sub-catchment resolution, and the transformative potential of generative AI for rapid scenario planning under deep climate uncertainty. Policy implications for national adaptation planning, infrastructure investment, and transboundary water governance are discussed.
climate resilience; artificial intelligence; stormwater management; wastewater treatment; non-stationarity; physics-informed neural networks; climate adaptation; urban flooding; water reuse; infrastructure planning
Improving access to women's health information through volunteer-led educational models
Access to accurate, timely, and culturally appropriate health information is fundamental to improving women's health outcomes and reducing health disparities. However, many women, particularly those in underserved and resource-constrained communities, continue to face barriers such as limited health literacy, socioeconomic constraints, inadequate healthcare infrastructure, cultural norms, and restricted access to reliable information sources. Volunteer-led educational models have emerged as a promising community-based strategy for addressing these challenges by delivering accessible, context-specific, and trusted health education through trained volunteers. This study examines the effectiveness of volunteer-led educational models in improving access to women's health information and enhancing health literacy. Using a comprehensive review of existing literature and evidence from community-based health education initiatives, the study explores the role of volunteers in disseminating essential information on maternal health, reproductive health, disease prevention, nutrition, and preventive healthcare practices. The findings indicate that volunteer-led educational interventions significantly improve women's knowledge, encourage positive health-seeking behaviors, strengthen community engagement, and increase awareness of available healthcare services. Furthermore, successful implementation depends on adequate volunteer training, sustained institutional support, culturally sensitive educational materials, and strong collaboration between healthcare providers, community organizations, and policymakers. The study concludes that volunteer-led educational models represent a cost-effective, scalable, and sustainable approach to expanding women's access to health information, particularly in underserved populations. Strengthening these community-based initiatives can contribute to improved health literacy, greater utilization of healthcare services, and enhanced health outcomes for women.
Women's health information, health literacy, volunteer-led education, community health education, women's health promotion, community engagement, health communication, health equity, public health, preventive healthcare.
Copyright (c) 2021 Author https://creativecommons.org/licenses/by-sa/4.0
Architecting Predictive Workforce Intelligence for Sustainable Stormwater Management Using Artificial Intelligence and Machine Learning
Sustainable stormwater management increasingly depends not only on the technical sophistication of hydrological infrastructure but on the human workforce capacity required to design, deploy, and sustain that infrastructure over its operational lifecycle. While prior research has separately examined machine learning applications in hydrological forecasting and in general workforce management, the deliberate architectural design of predictive workforce intelligence systems purpose-built for the stormwater management domain remains an underdeveloped area of inquiry. This paper presents a systematic architectural framework for predictive workforce intelligence in sustainable stormwater management, conceptualizing workforce capacity not as a peripheral operational constraint but as a first-class design variable within artificial intelligence (AI) and machine learning (ML)-driven environmental infrastructure systems. We define predictive workforce intelligence as the systematic application of AI/ML methods to forecast, optimize, and sustain the human capital systems—technical competencies, field deployment capacity, occupational safety, and long-term career sustainability—required for resilient stormwater infrastructure management under conditions of climate volatility and demographic workforce transition. The proposed architecture comprises four functional layers: a Workforce Data Fabric integrating heterogeneous human capital and infrastructure data sources; a Predictive Intelligence Layer applying supervised learning, survival analysis, and natural language processing to competency forecasting, attrition prediction, and deployment optimization; a Sustainability Optimization Layer applying multi-objective optimization balancing hydrological performance, workforce well-being, and long-term institutional capacity; and a Governance and Trust Layer ensuring explainability, equity, and labor ethics compliance. We examine the application of this architecture across the workforce lifecycle—recruitment and competency development, deployment and scheduling, retention and career sustainability, and knowledge transfer and succession planning—and present implementation evidence from pilot programs in three institutional contexts of varying technical maturity.
Predictive Workforce Intelligence, Artificial Intelligence, Machine Learning, Stormwater Management, Sustainable Infrastructure, Workforce Architecture, Attrition Prediction, Competency Forecasting, Environmental Engineering Workforce, Multi-Objective Optimization
Intelligent Enterprise Software Management: Combining SAP S/4HANA and Artificial Intelligence
The convergence of SAP S/4HANA—the world's most widely deployed in-memory Enterprise Resource Planning (ERP) platform—with artificial intelligence (AI) technologies represents a defining inflection point in enterprise software management. This article offers a comprehensive, multidisciplinary examination of how organisations can combine the transactional power of SAP S/4HANA with the cognitive capabilities of machine learning (ML), natural language processing (NLP), generative AI, and intelligent process automation to create what SAP terms the Intelligent Enterprise. We systematically review the AI technology landscape within the SAP ecosystem, encompassing SAP Business Technology Platform (BTP), SAP AI Core and AI Launchpad, the Joule generative AI copilot, and embedded intelligent scenarios across all major S/4HANA functional domains—Finance (FI/CO), Procurement and Materials Management (MM), Sales and Distribution (SD), Production Planning (PP), Plant Maintenance (PM), Quality Management (QM), and Human Capital Management (HCM).
SAP S/4HANA; artificial intelligence; intelligent enterprise; SAP BTP; machine learning; Joule; generative AI; enterprise resource planning; digital transformation; AI governance
Integrating Artificial Intelligence into SAP Ecosystems for Intelligent Enterprise Software Management
Enterprise Resource Planning (ERP) systems anchored by SAP SE have long served as the operational backbone of large and mid-sized organisations across manufacturing, logistics, finance, human capital management, and procurement. The emergence of artificial intelligence (AI) technologies—encompassing machine learning, natural language processing, computer vision, generative AI, and intelligent process automation—has opened transformative possibilities for embedding cognitive capabilities directly within SAP landscapes. This article provides a comprehensive examination of how AI is being integrated into SAP ecosystems, with particular focus on SAP Business Technology Platform (BTP), SAP AI Core, SAP AI Launchpad, RISE with SAP, and the generative AI capabilities introduced through Joule, SAP's natural language copilot. We analyse the architectural prerequisites for AI-ready SAP environments, map AI use cases across core ERP modules (FI/CO, MM, SD, PP, HR, SCM), and evaluate the organisational and technical challenges associated with AI adoption in complex, multi-tier SAP landscapes. Drawing on implementation case evidence and published vendor documentation, we further assess the implications for enterprise software management, including change management, skill development, data governance, and total cost of ownership. Our findings indicate that successful AI integration in SAP environments demands a holistic strategy that aligns technical architecture with business process redesign and organisational capability building. We conclude with a forward-looking framework—the Intelligent SAP Maturity Model (ISMM)—for guiding organisations through progressive stages of AI-enabled enterprise intelligence.
SAP; artificial intelligence; enterprise resource planning; SAP BTP; SAP AI Core; Joule; intelligent enterprise; machine learning; generative AI; digital transformation.
Digital Technologies and Educational Transformation: Bridging the Knowledge Divide
Digital technologies have reshaped education over the past three decades, promising to widen access, personalise learning, and democratise knowledge—yet the same technologies have also revealed and, at times, deepened a persistent divide between those who can benefit from them and those who cannot. This article reviews the transformation of education by digital technologies and examines the central challenge of bridging the knowledge divide. We distinguish the digital divide from the broader knowledge divide and frame the problem through a three-level model: a first-level divide in physical and material access to devices and connectivity; a second-level divide in the digital skills and meaningful use that turn access into benefit; and a third-level divide in the learning outcomes and knowledge that access and use are meant to produce. Against this framework we trace the evolution of educational technology from radio and correspondence courses to e-learning, learning-management systems, massive open online courses, mobile learning, and adaptive and artificial-intelligence-based systems, and we survey the affordances these technologies offer for access, personalisation, collaboration, and analytics. The COVID-19 pandemic, which at its peak disrupted the schooling of roughly 1.5 billion learners and forced an unprecedented global shift to remote instruction, serves throughout as a natural experiment that exposed the divide with unusual clarity: an estimated 826 million learners lacked a household computer and 706 million lacked home internet at the moment such access became essential. We analyse why access alone is insufficient, why technology does not transform education on its own, and what combination of infrastructure, affordable devices, digital literacy, teacher capacity, quality content, and inclusive policy is required to convert digital access into equitable learning. We conclude that bridging the knowledge divide is less a technological problem than a socio-educational one, and that digital technologies advance educational equity only when embedded in sound pedagogy, capable institutions, and deliberate, inclusive policy.
Remote Learning, Open Educational Resources,
Science and Technology in the Era of Artificial Intelligence: Innovations, Applications, and Future Challenges
Artificial intelligence (AI) has become the defining general-purpose technology of the present era, reshaping the methods of science, the capabilities of technology, and the structure of society. This article reviews science and technology in the era of AI across three dimensions: the innovations that define the era, the applications transforming research and industry, and the challenges that its rapid advance has raised. We trace the technical trajectory from symbolic artificial intelligence through the deep-learning revolution catalysed by large labelled datasets and graphics-processing hardware, to the recent rise of generative AI and foundation models, culminating in the public arrival of conversational systems such as ChatGPT in late 2022. We characterise the modern AI era as a layered stack in which abundant data and computation support machine-learning methods, which in turn yield capabilities in perception, language, generation, and decision-making that are deployed across an expanding range of applications. We survey those applications in science—where AI has accelerated protein-structure prediction, drug and materials discovery, and the analysis of vast experimental datasets—and in technology and industry, from computer vision and natural language processing to autonomous systems, smart manufacturing, and generative media. Finally, we analyse the future challenges that accompany these advances, organised around the goal of trustworthy and responsible AI: algorithmic bias and fairness, transparency and explainability, privacy, safety and robustness, accountability and governance, labour and economic disruption, and environmental cost. We conclude that AI’s transformative potential for science and technology is matched by the magnitude of the societal questions it raises, and that realising its benefits responsibly will require advances in governance, transparency, and human oversight as substantial as the technical advances themselves.
From Automation to Autonomy: The Evolution of Intelligent Technological Systems
Intelligent technological systems have undergone a profound evolution over the past century, progressing from fixed automation that executes predetermined sequences toward autonomy that enables systems to perceive, decide, and act under uncertainty with diminishing human involvement. This article reviews that evolution, clarifying the conceptual distinction between automation and autonomy, tracing the historical trajectory from mechanization to contemporary autonomous and agentic systems, and synthesising the frameworks used to characterise intermediate degrees of machine independence. We argue that the shift from automation to autonomy is not merely a matter of degree but a qualitative transition: automation follows fixed rules within bounded, predictable environments, whereas autonomy entails adaptive, goal-directed behaviour in open, uncertain ones, made possible by advances in sensing, computation, machine learning, and control. Drawing on established models—including the levels-of-automation taxonomies of Sheridan and Verplank and of Parasuraman, Sheridan, and Wickens, and the widely adopted six-level SAE J3016 standard—we present a generalised levels-of-autonomy framework and a structured comparison of automation and autonomy across dimensions of adaptability, decision scope, uncertainty handling, learning, and human involvement, illustrated through figures and tables. We then examine the enabling technologies and the domains in which the transition is most advanced, from self-driving vehicles and smart manufacturing to autonomous laboratories and software agents, before analysing the challenges that accompany increasing autonomy: trust and verification, accountability and liability, safety and predictability, human–autonomy teaming, and the persistent gap between the rhetoric and the reality of “full” autonomy. We conclude that the foreseeable future is not one of wholesale human replacement but of calibrated human–autonomy collaboration, and that the central challenge is designing systems whose autonomy is matched by trustworthiness, transparency, and appropriate human oversight.
Human–Autonomy Teaming, Robotics, Artificial Intelligence, Cyber-Physical Systems,
Digital Transformation in Science and Engineering: Trends, Challenges, and Future Directions
Digital transformation—the integration of digital technologies, data, and intelligent computation into the core of how work is conceived and conducted—is reshaping both the practice of science and the practice of engineering, and increasingly dissolving the boundary between them. This article provides a comprehensive review of digital transformation across science and engineering, charting its principal trends, the challenges that impede it, and the directions in which it is heading. We first distinguish digital transformation from the narrower notions of digitization and digitalization, then organise the field around four interlocking pillars: a data foundation built on big data, the data-intensive “fourth paradigm,” and the FAIR and open-science movements; a layer of computational and intelligent methods spanning high-performance and cloud computing and the rapid diffusion of artificial intelligence and machine learning; the convergence of the physical and the digital through digital twins, cyber-physical systems, the Internet of Things, and the Industry 4.0 paradigm; and the automation of discovery and production through self-driving laboratories, robotics, and additive manufacturing. Across these pillars we identify a common set of challenges that determine whether transformation succeeds in practice: data quality and interoperability, cybersecurity, the absence of standardization, the reproducibility of computational results, shortages of interdisciplinary skills, and pronounced inequities in access to data and compute. We then survey emerging future directions, including the human-centric and sustainability-oriented vision of Industry 5.0, cognitive digital twins, agentic artificial intelligence, edge intelligence, and the industrial metaverse. We conclude that digital transformation is best understood not as the adoption of any single technology but as a convergent, socio-technical reconfiguration of research and engineering practice, whose ultimate success depends as much on data governance, standards, security, and human capacity as on the underlying technical advances.
Deep Learning Approaches for Identifying Fraudulent Activities in Digital Payment Networks
The migration of commerce onto digital payment networks has been accompanied by an escalation in the scale, speed, and sophistication of financial fraud, exposing the limits of rule-based systems and classical machine-learning classifiers that score each transaction in isolation. Over the past decade, deep learning has emerged as the dominant methodological response, offering models that learn discriminative patterns, temporal dynamics, latent normality, and relational structure directly from transaction data. This article provides a comprehensive review of deep learning approaches for identifying fraudulent activities in digital payment networks, organised as a taxonomy of architectural families and a comparative analysis of their respective strengths and limitations. We examine discriminative architectures (feedforward, convolutional, and recurrent networks); reconstruction- and generation-based approaches (autoencoders, variational autoencoders, and generative adversarial networks) used for anomaly detection and for synthesising minority-class data; attention-based transformers that model long-range behavioural dependencies; graph neural networks that capture the relational structure of organised fraud rings; and the hybrid and ensemble systems that increasingly combine these components with gradient-boosted trees. Across this taxonomy we identify a set of cross-cutting challenges that determine practical success more than any single architecture: extreme class imbalance, non-stationary fraud driven by adversarial adaptation, the latency constraints of real-time authorisation, the privacy limits on data sharing, and the demand for explainable decisions in a regulated domain. Synthesising reported benchmark evidence, we find that no single architecture dominates universally; that sequence-aware and relational models offer the clearest advantage where behavioural and network signal is present; that imbalance handling and deployment engineering frequently matter more than model choice; and that hybrid pipelines presently represent the strongest practical solutions. We conclude that the field is converging on hybrid, imbalance-aware, explainable systems, and that the decisive research frontier lies less in raw detection accuracy than in robustness, real-time viability, privacy preservation, and interpretability.
Predicting the Pivot: Real-Time Fraud Prevention in High-Volume Payment Gateways Using Graph Neural Networks
High-volume payment gateways must decide whether each transaction is legitimate within a few tens of milliseconds, while the fraud they seek to catch is rare, adversarial, and increasingly organised into coordinated rings rather than isolated events. Conventional fraud controls—rule engines and tabular gradient-boosted classifiers—score each transaction in isolation and therefore miss the relational signal that distinguishes a fraud ring from ordinary commerce. This article evaluates whether graph neural networks (GNNs), and specifically a temporal, heterogeneous, inductive architecture designed for streaming inference, can catch fraudulent transactions as they happen without violating the latency budget of a production gateway. We formalise real-time gateway fraud detection as inductive node-and-edge classification over a dynamic, attributed transaction graph whose nodes represent cards, merchants, devices and network identifiers, and we propose a Temporal Relational Graph Attention model with a per-node memory module (TR-GAM) that combines time-aware message passing, relation-specific attention, inductive neighbour aggregation, and label-aware sampling for extreme class imbalance, deployed through a Lambda-style pipeline that separates batch training from low-latency streaming scoring. The architecture is assessed against tabular and graph baselines—LightGBM, GCN, GAT, GraphSAGE, CARE-GNN, PC-GNN and a standard Temporal Graph Network—on the public IEEE-CIS, Elliptic, Amazon and YelpChi benchmarks, using area under the ROC and precision–recall curves, macro-F1, recall at a fixed false-positive operating point, and per-transaction inference latency. Drawing on a reproduction protocol and a synthesis of reported benchmark results, the evaluation indicates that temporal relational GNNs deliver materially higher recall at the low false-positive rates that production demands, that their advantage is largest precisely where fraud is organised, and that inductive, memory-based designs can sustain sub-100-millisecond scoring at gateway scale. We discuss the principal obstacles to deployment—neighbour-explosion latency, concept drift, label delay, and adversarial camouflage—and argue that the architecture’s real-time viability, not its offline accuracy alone, is the decisive criterion for fraud prevention.
Financial Machine Learning, Anomaly Detection
Artificial Intelligence in Scientific Discovery: Transforming Research Methodologies Across Disciplines
Artificial intelligence (AI) has moved from a peripheral computational aid to a central instrument of scientific discovery, reshaping how researchers across disciplines generate hypotheses, design experiments, analyse data, and interpret results. This article offers a comprehensive, cross-disciplinary review of how machine learning, and deep learning in particular, is transforming research methodologies, synthesising landmark advances from the life sciences, chemistry and materials science, the formal and physical sciences, and the data-rich observational fields of earth and climate science. We organise the review around a central thesis: AI is not merely accelerating existing methods but reconfiguring the scientific method itself, augmenting the traditional hypothesis-driven paradigm with data-driven, generative, and increasingly autonomous modes of inquiry. Drawing on the flagship literature, we trace the protein-structure revolution catalysed by AlphaFold; the generative design and autonomous robotic synthesis of novel molecules and materials; the use of learning systems to guide mathematical intuition, discover algorithms, and control physical apparatus; and the emergence of neural weather and climate models that rival established numerical simulation. We further examine how AI is transforming the research workflow itself through automated literature synthesis, hypothesis generation, and the first generation of autonomous “self-driving” laboratories and large-language-model research agents. Alongside these advances, the review analyses the methodological hazards that accompany them—data leakage and a reproducibility crisis, the opacity of learned models, the risk of mistaking predictive accuracy for scientific understanding, and inequities of access to data and compute. We conclude that AI augments rather than replaces human scientists, that its responsible integration demands new norms of transparency and validation, and that the discipline-spanning convergence on shared learning methods is itself among the most consequential methodological shifts in the modern history of science.
Generative Models, Reproducibility, Interdisciplinary Research,
Copyright (c) 2024 International Journal of Science, Technology & Society
Regulating the Virtual Agora: A Comprehensive Review of Policy Frameworks Addressing Disinformation and Corporate Content Moderation
The digital public sphere—the “virtual agora” in which contemporary political deliberation increasingly takes place—is privately owned, algorithmically curated, and global in reach, yet governed by an emergent patchwork of national and supranational rules of strikingly divergent design. This article offers a comprehensive, comparative review of the policy frameworks that have crystallised between 2016 and 2025 to address two intertwined problems: the proliferation of online disinformation and the largely discretionary content-moderation practices of dominant technology firms. Drawing on legal instruments, regulatory documents, judicial decisions, and the platform-governance scholarship, the review synthesises five families of response: the European Union’s systemic-risk, co-regulatory model under the Digital Services Act and its integrated Code of Conduct on Disinformation; the United States’ constitutionally constrained approach anchored in the First Amendment and Section 230 of the Communications Decency Act; the United Kingdom’s duty-of-care regime under the Online Safety Act 2023; a heterogeneous cluster of national statutory-liability and judicial-order models exemplified by Germany, France, Brazil, Singapore, India and the abandoned Australian bill; and the self-regulatory turn within firms themselves, dramatised by Meta’s January 2025 abandonment of third-party fact-checking in favour of community-driven moderation. The analysis identifies three cross-cutting tensions that structure the entire field: the definitional instability of “disinformation,” the persistent gap between transparency and genuine accountability, and the unresolved trade-off between harm mitigation and freedom of expression. The review concludes that no jurisdiction has achieved a durable equilibrium, and that the period under study marks a decisive shift from voluntary self-regulation toward enforceable co-regulation in Europe even as the United States and major platforms move in the opposite direction. It argues for procedurally grounded, rights-protective frameworks that prioritise algorithmic transparency, structural separation of moderation decision-making, and meaningful user participation over substantive adjudication of truth.
Algorithmic Bias in Municipal Decision-Making: A Case Study of AI Deployment in Urban Public Service
Artificial intelligence systems are increasingly embedded in municipal governance, influencing how cities allocate resources, enforce regulations, and deliver public services. This article investigates the manifestation and consequences of algorithmic bias in three urban public service domains: predictive policing, welfare benefit eligibility screening, and housing inspection prioritization. Drawing on a multi-city case study methodology spanning twelve North American and European municipalities from 2018 to 2024, we identify four recurring structural conditions that amplify bias in municipal AI: historically skewed training data, opaque procurement processes, the absence of community input in system design, and inadequate post-deployment auditing. Our findings demonstrate that algorithmic outputs often replicate and entrench pre-existing socioeconomic and racial inequalities rather than remediate them. We propose a Municipal AI Accountability Framework (MAAF) encompassing algorithmic impact assessments, participatory design requirements, mandatory transparency disclosures, and independent third-party auditing. The study concludes with policy recommendations directed at legislators, city administrators, and technology vendors.
predictive policing; smart cities; algorithmic accountability; urban inequality
Study on the Comparative Performance Assessment of Distillation method by using a Conventional Method and Elix Technology
In this study, experiments were conducted on feed water and distill water generated from simple solar still and from elix technology; water quality analysis was done in laboratory to check the contaminants removal efficiency from raw water samples. Changes in the water quality may causes error in the compromised experimental results, contaminated reagents and also may change the accuracy of the equipment. The quality of water used in the laboratory is critical for the success of the tests performed. Water quality parameter such as pH, EC, TDS, COD, total alkalinity, total hardness, free residual chlorine, sodium, turbidity, chloride, sulphate, floride, Total and Fecal coliform and heavy metal like Pb, Zn, Cu, Fe, Mn, were analysed. Distilled water is one of the essential components for chemical and biological analysis in the field of analytical and medical testing. Many distillation methods are available but they are either energy intensive or use renewable (solar) energy like conventional solar still. In this study, performance analysis was conducted for Non-conventional (reverse osmosis and electro-de-ionization hybrid) and conventional (solar still) distillation units. The values obtained were within the standard range and it was concluded that the results of Elix water testing from processing unit were found more promising for research purpose.
Solar still, Distilled water, Reverse osmosis, Electrode de-ionization
Novel Utilization of Biofertilizers in Crop and Soil Improvement
The study was carried out to investigate the yield and soil quality of wheat (Triticum aestivum L.) and moong (Vigna radiate L.) crop. In order to obtain high precision in the results of the present investigation, the research trials were conducted for two consecutive years 2008-09 and 2009-10 under field conditions at Artoni (Agra) site. The experiment was laid out in RBD with five microbes (Azospirillum, Azotobactor, PSB, Azolla and Rhizobium) and four levels (120 & 60 kg N for wheat and 25 & 10 kg N for moong crop) of nitrogen. The results of this experiment proved that T2 treatment (Azospirillum +60 kg N ha-1) showed the best outcome during both the years for wheat crop and in moong crop. The growth and yield parameters i.e. plant height, no. of branches plant-1 no. of pods plant-1, pod length and grain yield were observed and treatment T7 (Rhizobium +10 kg N ha-1) was found to be better combination than others. The soil in respect to its physico-chemical properties showed an improvement due to the use of biofertilizers when compared to control and farmers practice.
Biofertilizer, Triticum aestivum L., Vigna radiata L, Azospirillum, Azotobactor, PSB, Azolla and Rhizobium
Pharmacological Evaluation of Saccharum Officinarum Linn. as Anti-ulcer Agent
The purpose of the study was to elucidate the antiulcer activity of stem of Saccharum Officinarum (Poaceae) in albino rats against aspirin induced gastric ulcer, ethanol induced gastric ulcer and pylorus ligation induced gastric ulcer models. Groups of rats were fasted overnight, received ranitidine (20 mg/kg) as a standard and plant stem juice extract at dose of 0.75 mL/100 gm and 1.5 mL/100 gm as a treatment against aspirin induced gastric ulcer, ethanol induced gastric ulcer and pylorus ligation induced gastric ulcer. The treatment produced significant protection of ulcer induced by aspirin, ethanol and pylorus ligation induced ulcer. The extract also reduced ulcer index, volume of gastric content, free and total acidity, suggesting that extract possesses significant anti-ulcer activity.
Anti-ulcer, Aspirin, Eth anol, pylorus ligation, Saccharum Officinarum
Methane Flux Estimation in MSW Open Dump Site by In-Situ Measurement and IPCC (DM and FOD) Model
This paper deals with the use of different methods to predict methane generation on an open dump site. Methane recovery data obtained on site as part of a research program being carried out at the Devguradia dumpsite, Indore, is analyzed and used to obtain field methane emission in three seasons. Field data from Municipal solid waste (MSW) dump site are presented and discussed measured in-situ technique i.e. using close chamber methods. The Intergovernmental Panel on Climate Change (IPCC) Default Method (DM) and First order Decay (FOD) method is used to estimate methane generation. It is found that despite the assumptions and the simplicity of the adopted procedures, the values methane obtained are slightly varying to those measured in the field. The values obtained were 4.5, 2.9 and 0.35 Gg/y respectively.
Methane, First order decay method, Default method, IPCC
Application of Plant Growth Promoting Rhizobacteria in Growth Enhancement of a Multipurpose crop Crotalaria Juncea (Sunhemp) in Saline Soil
Salinity is a major abiotic stress which limits the crop productivity. In this study salt tolerant bacteria were isolated from root nodules of Crotalaria juncea plant collected from different salt affected sites. Salt tolerance activity of all the isolates was checked and it was observed that 47% of the isolates showed up to 8% salt tolerance. On the basis of morphological, biochemical and physiological characterization isolates were found to be Gram negative, rod shaped and motile bacteria that belong to rhizobia. All the isolates also showed nodulation in host plant. Most of the isolates were positive for various plant growth promoting attributes. On the basis of salt tolerance and plant growth promoting characterization isolate CR6 was selected for further studies. CR6 showed positive results for phosphate and zinc solubilisation, and was able to produce indole acetic acid (IAA), siderophore and exopolysaccharides (EPS). CR6 also showed nitrogen fixation and ACC deaminase activity. CR6 was applied on Crotalaria plant to enhance growth in saline soil. After application of CR6 seed germination rate and various growth parameters such as root length, shoot length, nodule number, fresh weight, dry weight, leaves count, chlorophyll and proline content of plants were enhanced in comparison to control, hence CR6 can be used as an efficient bioinoculant to enhance crop growth in saline soil.
Abiotic stress, soil salinity, PGPR, rhizobia, Crotalaria juncea
Cosmoceutical and Therapeutic Potential of Isotretinoin Nanoparticulate Gel in Management of Acne
This study intended to develop and evaluate isotretinoin (ITR) loaded nanoparticles taking chitosan as a polymer of interest. Ionic crosslinking method was utilized to prepare nanoparticles. Nanoparticles were formulated using varying ratios (chitosan: tripolyphosphate) and evaluated for its size, distribution of size, zeta potential, percentage of ITR entrapped within nanoparticles, in vitro drug release and its stability under accelerated conditions. The prepared nanoparticles (NPs) were spherical, white in color and free flowing, 321± 4.5 nm was the average size of optimized chitosan nanoparticles and it was able to entrap 88.76 ± 3.5%. The outcomes assured vast promise of the CNs of ITR (optimized) in management of acne and also increasing the therapeutic efficacy, thus establish to be a promising, effective and patient compliant formulation.
Biodegradable, Tripolyphosphate, Ionic gelation technique, Acne, Chitosan
Issues and Challenges in Functioning of Panchayati Raj Institutions for Sustainable Development: A Case Study
Sustainable developmental goals (SDG’s) are new global level of goals, with targets and indicators that 193 member countries of United Nations in the world have mutually set and agreed upon. There are 17 goals on which the entire world is focusing at present to ensure sustainable development. For attaining these goals, local self-governance is used at the grassroots levels and in India the machinery is Panchayati raj Institutions. These institutions operate from central and state government level and include all the villages in it. These villages have panchayats and it is maintained by Gram Pradhans i.e. elected representatives, Secretary and other members of the gram panchayats. Though a lot is being done at grassroot level by these Gram Pradhans, under the mechanism of Panchayati raj institution and other administrative bodies, however it is not free from challenges. The present case study is a descriptive study which focuses attention to identify different challenges the elected representatives experience, in Panchayati raj institutions in India, which contribute directly for the sustainable development.
Sustainable development Panchayati Raj Institutions, Women, India, Challenges
Indian Religious Belief and Its Impact on Meat Industry
The livestock provides food items such as milk, meat and eggs for human consumption that are rich source of protein, mineral and vitamins and good for health. Livestock production is a vital activity in rural areas which provides employment, livelihood and income for farmers, rural poorer and weaker sections of society. India has religious belief that most of the Hindu community does not eat beef. They do not slaughter cattles (cow and its progeny). This is the main reason that Buffalo is surplus for export unlike goat/sheep/ poultry meat. This leads to India as a number 2 exporter of beef in the world. Livestock business provides employment to about 8.8 % of the population in India. Livestock sector is contributing 4.11% GDP and 25.6% of total Agriculture GDP of country. Governments and industries must prepare for meeting demand of meat in the country with long run policies and investments to satisfy ever rising consumer demand, improve nutritional status, generation of income opportunities and alleviate environment stress.
Molecular Pathways of Ischemic Stroke and Advanced Therapeutic Strategies
Ischemic stroke is a leading cause of death and permanent disability. This disease may affect any age group and especially in old age and pregnancy. All the responsible mechanisms are yet not completely understood. There is limited therapeutic intervention beyond prevention, yet tremendous progress in understanding cause of stroke at molecular level has been going on. A lot of advancement has occurred in the prevention and treatment of stroke during the past decade. In this review an update work of causeways of stroke and its therapeutic approaches have been discussed. The relevance of excitotoxicity (role of glutamate receptor), inflammation, ischemic penumbra, apoptosis, to delayed mechanisms and, damage and treatment strategies have been hasted out. Although the results among clinical studies, conflict regarding several experimental data of different therapies, and it may improve neurological outcome after acute cerebral ischemia. Along this several other interventions and new technologies such as stroke detector microwave helmet are being assessed and many other advanced techniques developed by researchers. Even the development of other novel and new treatment strategies (regarding molecular pathways and risk to benefit therapeutic ratio) for stroke are still required in future for better treatment.
Exitotoxicity, Molecular mechanisms, Stroke, Treatment strategies
Recent Advances in Temperate Fruit Crop Improvement
Globally India is the second largest producer of fruits; however the productivity is significantly low as compared to advanced countries. The majority of the temperate fruits and nuts are being cultivated in the north-western Himalayan and small areas are lies in north eastern parts. High altitude and temperate regions are capable to produce good quality fruits. But the foothill where the chilling is insufficient adversely affects the yield and quality. In India, majority of the temperate fruits were introduced from other parts of the world and some are indigenous to India like walnuts and wild apple species. Therefore, the improvement of the temperate fruits and nuts were taken place mainly through introduction of promising cultivars. The exotic cultivars are still the backbone of fruit production in India particularly for apple, pear, peach, plum and cherry. Promising varieties were introduced from USA, UK, Germany, Italy, France, Australia, New Zealand, Hungary and Bulgaria and were evaluated for commercial exploitation. However, in walnutand almonds, production is mainly from Indigenous cultivars/ local land races. These consisted of non-descriptive trees and highly variable in yield and quality traits. Recently, several varieties have been developed in India in apple, peach, plum, apricot, cherry and walnut. The potential of these varieties are immense for commercial exploitation. The paper attempt to document the accomplishment made in temperate fruit and nut improvement in perspective to Indian Scenario. The information is valuable for breeders and academician for further studies.
Temperate Fruits, Nuts, Breeding, Biotechnology, Processing